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Lookup NU author(s): Dr Gavin LowesORCiD, Professor Jeffrey Neasham, Richie Burnett, Dr Benjamin SherlockORCiD, Professor Harris Tsimenidis
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This paper presents the development of a low-energy passive acoustic vessel detector to work as part of a wireless underwater monitoring network. The vessel detection method is based on a low-energy implementation of Detection of Envelope Modulation On Noise (DEMON). Vessels produce a broad spectrum modulated noise during propeller cavitation, which the DEMON method aims to extract for the purposes of automated detection. The vessel detector design has different approaches with mixtures of analogue and digital processing, as well as continuous and duty-cycled sampling/processing. The detector re-purposes an existing acoustic modem platform to achieve a low-cost and long-deployment wireless sensor network. This integrated communication platform enables the detector to switch between detection/communication mode seamlessly within software. The vessel detector was deployed at depth for a total of 84 days in the North Sea, providing a large data set, which the results are based on. Open sea field trial results have shown detection of single and multiple vessels with a 94% corroboration rate with local Automatic Identification System (AIS) data. Results showed that additional information about the detected vessel such as the number of propeller blades can be extracted solely based on the detection data. The attention to energy efficiency led to an average power consumption of 11.4 mW, enabling long term deployments of up to 6 months using only four alkaline C cells. Additional battery packs and a modified enclosure could enable a longer deployment duration. As the detector was still deployed during the first UK lockdown, the impact of COVID-19 on North Sea fishing activity was captured. Future work includes deploying this technology en masse to operate as part of a network. This could afford the possibility of adding vessel tracking to the abilities of the vessel detection technology when deployed as a network of sensor nodes.
Author(s): Lowes GJ, Neasham J, Burnett R, Sherlock B, Tsimenidis C
Publication type: Article
Publication status: Published
Journal: Journal of Marine Science and Engineering
Year: 2022
Volume: 10
Issue: 2
Online publication date: 12/02/2022
Acceptance date: 09/02/2022
Date deposited: 24/03/2022
ISSN (electronic): 2077-1312
Publisher: MDPI AG
URL: https://doi.org/10.3390/jmse10020248
DOI: 10.3390/jmse10020248
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